Difference in presentation, outcomes, and hospital epidemiologic trend of COVID-19 among first, second, and third waves: a review of hospital records and prospective cohort study

Background: This study aimed to examine the differences in epidemiologic and disease aspects among patients with coronavirus disease-19 (COVID-19). Methods: The authors reviewed the hospital records between April 2020 and September 2021 and followed up on the patients for post-COVID complications. Findings: Older adult patients were predominantly affected during the third wave, and middle-aged patients were predominantly affected during the first and second waves. Men were predominantly admitted, considering the three waves, although more women were admitted in the second wave. Cough was more common in the second and third waves than in the first wave 522 (59.7%). Respiratory distress was the most common in the third wave, 251(67.1%), and least common in the first wave, 403 (46.1%). Anosmia was more common in the third wave 116 (31.2%). In the third wave, patients presenting in a critical state 23 (6.2%) and with severe disease 152 (40.8%) were more common. The hospital admission median (IQR) was longer in the first wave, 12 (8–20), than in other waves. More patients were admitted in the first wave (52%) than in the other waves, and patients received more oxygen in the third wave (75%) than in the other waves. Death occurred more commonly in the first wave (51%) than in the other waves. The positivity rate was higher in the third wave (22.8%) than in the other waves. In the third wave, the positivity rate was higher in women (24.3%) than in men. Post-COVID cough increased in the second wave, and fatigue was higher in the third wave than in the other waves. Tiredness and memory loss were greater during the second wave than in other waves. Conclusion: The authors found differences in the presentation, outcomes, and hospital epidemiologic trend of COVID-19 among the three waves.

The WHO classified the phases of the pandemic as phases 1-3 (predominantly animal infection, few human infections), phase 4 (sustained human-to-human infection), phases 5 and 6 (widespread HIGHLIGHTS • The hospital epidemiologic trend differed in three coronavirus disease-19 waves; notably, the first wave lasted longer. • The symptom cluster differed among the three waves.
• There were disparities in the frequencies of the positivity test and death rates.
human infection), post-peak (possibility of recurrent events), and post-pandemic (disease activity at seasonal level) (https://www. worldometers.info/coronavirus/). Most of the worst pandemics lasted for several years (https://www.who.int/influenza/resources/docu ments/pandemic_phase_descriptions_and_actions.pdf). After studying the transmission dynamics of SARS-CoV-2, researchers postulated that it would reach long-term circulation within the next 5 years (https://www.history.com/news/pandemics-end-plaguecholera-black-death-smallpox). Experience in the last 2 years has revealed that resurgence as 'waves' is common (https://www.world ometers.info/coronavirus/), and the overall pattern of the coronavirus pandemic has been a series of increased COVID-19 waves, which gradually declines. Several outbreaks of illnesses have occurred in various parts of the world. Europe entered its third phase in March 2021 [4] . According to the WHO, the COVID-19 variant initially detected in the UK has spread widely in at least 27 European countries, with Denmark, Italy, Ireland, Germany, France, the Netherlands, Spain, and Portugal being the most affected (https:// www.theguardian.com/world/2021/mar/22/lockdowns-return-exten ded-third-wave-covid-europe) and at that time the United States was likely to experience the fourth wave (https://time.com/5951490/ covid-fourth-wave/). The seasonal variation in transmission, duration of immunity, degree of cross-immunity between SARS-CoV-2 and other coronaviruses, intensity, and timing of control measures [4] , and not the least viral mutation can all explain the wave and gradual decline of cases (waves of the disease) (https://www.who.int/csr/don/ 31-december-2020-sars-cov2-variants/en/). SARS-CoV-2 has mutated at a pace of about 1-2 mutations per month throughout the current global crisis (https://www.nature.com/articles/d41586-020-02544-6). A variant of SARS-CoV-2 with the D614G mutation emerged in China in late January and early February 2020 and immediately became the dominant form of the virus circulating globally (https://www.who.int/csr/don/31-december-2020-sars-cov2variants/en/). Subsequently, several mutations were recognized as mutations of concern, naming the UK variant known as 501Y. V1, VOC 202012/01, and B. It is essential to understand the variations in how COVID-19 is presented, the resulting outcomes, and the epidemiological trends in hospitals during different waves of the pandemic. Therefore, this study aimed to systematically and scientifically observe the difference in the presentation and outcome of patients with COVID-19 between the first, second, and third waves. Additionally, we examined the positivity rate, hospital admission rate, and death rate in the outpatient department.

Materials and methods
This study aimed to examine differences in the epidemiology of COVID-19 in the first, second, and third waves. We aimed to find out if there were any variations in the symptoms, disease severity, case fatality, length of hospital stays, frequency of patients requiring oxygen therapy, and referrals to the ICU. We reviewed the outpatient data for clinic attendance, hospital admission rate, and death in the outpatient department. We examined the virology laboratory data to observe differences in the number of tests and positivity rate with the age and sex variation. We reviewed inpatient hospital data for rates of hospital admission and death. We also assessed the differences in complications of COVID-19 in patients followed up for at least 6 months after hospital discharge during the first, second, and third waves of COVID-19. The work has been reported in line with the STROCSS (strengthening the reporting of cohort, cross-sectional and case-control studies in surgery) criteria [7] , Supplemental Digital Content 1, http://links.lww.com/MS9/A170. This study was registered at www.researchregistry.com.

Study area and period
The study was conducted at a tertiary care hospital between 1 January 2021 and 20 June 2022.

Study design
We reviewed the hospital records of outpatients, inpatients, and virology departments. From that patient, some sampled patients (200 patients for each wave) were prospectively followed up for the post-COVID symptoms at least 6 months after discharge. The follow-up was given by telephonic interview following a prespecified telephonic interview guide.

Study population
The study population was the admitted patient and the patient who responded to the telephone at least 6 months after follow-up.

Eligibility criteria
The study included patients' records containing important demographics such as age, sex, area of residence, telephone number, complete treatment sheet, admission and discharge date, and at least a brief history. Patients more than 18 years of age, with real-time reverse transcriptase-polymerase chain reaction positive test results, irrespective of the severity of the disease, admitted between April 2020 and September 2022 were included in this study for analysis and follow-up. Patients who did not meet the study's criteria, such as those with incomplete or inconclusive data or those outside the specified time period, were excluded from the study.

Sample size determination
The sample size was calculated using the formula below, and the hypothesis was that there was no variation in presentation between the COVID-19 waves in Bangladesh during the pandemic: Here, Z α = 1.96, Z value of standard normal distribution at 95% CI, P1 = prevalence of severe infection in the first wave, P2 = prevalence of severe disease in the second wave, RR = 1.5 (risk ratio), P2 = 0.12; in one study in our country, the prevalence of severe infection was 11% [7] , P1 = RR × p 2 = 0.16, Z β = 0.84 at 80% power, P = (p1 + p2)÷2 = 0.13. Thus, the estimated sample size was 196 for each wave.

Sampling technique and procedure
Cluster sampling methods were used to select the patients to observe the difference in the presentation and outcome of COVID-19. We compiled a list of all wards where patients with COVID-19 had been admitted. We selected one male ward and one female ward randomly. We subsequently reviewed all records that met the inclusion and exclusion criteria. We stratified the patient according to the time of the wave and tried to contact every consecutive patient for post-COVID follow-up who elapsed 6 months after discharge until we recruited 200 patients in each wave. For those who lost the follow-up, we excluded them from the study.

Study variables
The variables included in the study were:

Demographic variable
Age of the respondents in years and sex.

Comorbid conditions
We observed pre-existing health conditions, particularly hypertension, diabetes, ischemic heart disease, pulmonary diseases such as bronchial asthma or COPD, and renal disease before being infected with COVID-19.

COVID-19 severity
The severity of COVID-19 is contingent upon the symptoms one undergoes, which can range from being asymptomatic to experiencing life-threatening conditions (asymptomatic, mild, moderate, severe, or critical).

Duration
Duration between symptom onset and hospital admission, length of hospital stay.

Outcome-related questions
Discharged and death, oxygen requirement, ICU referral.

Functional impact
We examined functional implications in the post-COVID state for at least 6 months for overall functional status, fatigue score, and depression score.

Operational definition
Confirmed COVID-19 and its different severities were defined according to the WHO and National Guidelines [8,9] .

Duration of hospital stay
The total period from the day of admission to the day of discharge.

Waves of infection
The first, second, and third waves extend between April 2020 and January 2020, February 2021 and May 2021, and June 2021 and September 2021, respectively [10,11] .
Post-COVID-19 condition: according to the Center for Disease Control, USA [12] Post-COVID conditions include a wide range of new, returning, or ongoing health problems that people experience after contracting the SARS-CoV-2 causing COVID-19.

Post-COVID fatigue
The diagnostic criteria for post-COVID-19 fatigue were developed according to the United States Institute of Medicine symptom criteria [13] . The Chalder Fatigue Scale [14] , the Karnofsky Performance Status Scale [15] , and the Depression Scale were used to assess fatigue severity, overall functional status, and depression [16] .

Data collection instrument
For patients admitted to the hospital, data were collected on a data collection sheet. A telephone directory was used for post-COVID-19 follow-up. We collected data from reports of the outpatient, inpatient, and virology departments.

Data collection procedure
We collected all patients' files from the hospital record unit in the selected ward. Each file was evaluated by one data collector and researcher. The completed data collecting sheet contains a list of all available information. We obtained no specific procedure for missing data and excluded files that did not contain essential information, such as patient demographics, admission date, and discharge date. We collected the phone number from the hospital records and interviewed the patients for post-COVID-19 complications at least 6 months after recovery. We collected data from the outpatient, inpatient, and virology departments using the Excel datasheet, which contains the patient's name, age, sex, date of attendance, outcome, admission, death, discharge, positive or negative status, etc.

Data quality control
Each datasheet was reviewed by two researchers; disagreements were resolved through discussion. We removed responses where respondents provided contradictory answers. Because each patient was coded and had a specified hospital record number, there was no scope for double responses.
Data processing and analysis R (v4.1.1) was used to process data. We performed a network analysis with a tidy verse and q graph. Qualitative data with normal distribution were expressed as means (SD), while nonnormal data were expressed as medians (IQR). We divided the respondents into groups of first, second, and third waves. We used the χ2-test to calculate qualitative data, one-way ANOVA to calculate normally distributed quantitative data, and Kruskal-Wallis test to calculate skewed quantitative data. As needed, we used a post hoc analysis with Bonferroni adjustment, Dunn test, and Tukey's test. We determined epidemiological trends in the EXECL sheet. The P-value for statistical significance was set at <0.05. We did not impute any missing values, and they were included in the analysis.

Ethical consideration
The Institutional Ethical Committee of the respected Hospital approved this study (ERC-DMC/ECC/2021/55). As we mainly reviewed the hospital data, there was no need for written consent. The patients provided verbal consent for post-COVID follow-up. Table 1 Demographic characteristics and comorbidity of the admitted patients in the three waves

Results
We reviewed 1766 patient records and included 1595 patients' data for analysis. From these patients, we followed 600 patients over the telephone for post-COVID-19 complications. Between April 2020 and September 2021, 38 578 patients visited the outpatient department, 24 501 patients were admitted, and 57 857 were tested at the virology department (Supplement 1, Supplemental Digital Content 2, http://links.lww.com/MS9/A171). The mean age (SD) of the hospital-admitted patients was 47.9 (15.67) years, with patients in the second and third waves being higher than those in the first wave (P < 0.001).
Cough was more prevalent in the second 288 (65.5%) and third waves 290 (78%) than in the first wave 522 (59.7%). Running nose was most prevalent in the first wave, 143 (16.4%), and least prevalent in the second wave, 16 (4.6%). Respiratory distress was the most prevalent in the third wave, 251 (67.1%), and the least prevalent in the first wave, 403 (46.1%). Sore throat occurred more prevalent in the first wave 198 (22.7%) than in the other waves. Diarrhea 60 (16.1) and vomiting 59 (15.9%) were the most prevalent in the third wave. Anosmia was more prevalent in the third wave 116 (31.2%) than in other waves and less frequently in the second wave 27 (7.8%) than in other waves. However, headache was more prevalent in the third 82 (22.5%) and first waves 145 (16.6%) than in the second wave. Body aches occurred more commonly in the second 87 (25%) and third 92 (24.6%) waves than in the first wave ( Table 1).
Patients that presented in a critical condition 23 (6.2%) and severe disease 152 (40.8%) were more prevalent in the third wave than in other waves. In the second wave, most cases were mild 216 (61.9%) ( Table 1).
Different comorbid conditions, notably diabetes, hypertension, asthma, ischemic heart disease, and renal disease, differed in the three waves (Table 1).

Symptoms cluster in COVID-19
We found three clusters of symptoms among the admitted COVID patients: (1) fever, cough, and respiratory distress; (2) anosmia, headache, and anorexia; (3) sore throat and running nose. The symptom clusters differed for the three waves. In the first wave, we found three clusters: (1) respiratory distress and cough; (2) anosmia and anorexia; (3) running nose and sore throat. In the second wave, we found a cluster of headaches, running nose, sore throat, and hoarseness of voice. In the third wave, we found three clusters: (1) anorexia and anosmia; (2) fever and cough; and (3) headache, diarrhea, and vomiting (Fig. 1).
Confirmed COVID-19 cases were higher (15.7%) in the second wave than in the other waves; the admission rate of 73.7% and the death rate of 1.3% were higher in the second wave than in other waves. The proportion of confirmed COVID-19 cases was higher among women in all waves. In the third wave, the admission rate among women was higher than among men. The number of positive cases was higher in those below 40 years in the first wave and above 60 years in the third wave. The admission rate of patients below 40 years was lower in the third wave. In the second wave, the admission rate among the 40-60-year age group and above 60 years age group was higher than in other waves. In the second wave, the death rate was higher among men, 1.4%, than among women, 1.3%. In the second wave, the death rate was high in the 40-60-year age group and above the 60-year age group. The admission rate was higher in patients aged above 60 years in all three waves.
Death was more common (51% of total death) in the first wave.
The patient was tested more frequently in the first and third waves than in the second wave. The positivity was high in the third wave, at 22.8%. In the third wave, the positivity rate was high among women, 24.3%. The incidence rate of patients above 60 years was high in the first and third waves, at 27.3% (Table 4).
The daily COVID-19 infection rate was higher in the third wave, and the duration of the second wave was short. The first wave lasted longer than the other wave, with an elevated and gradual infection rate decline.
The rate of testing was equal for all three waves. The positivity rate was higher during the third than in the first and second waves (Fig. 2, Supplement 2, Supplemental Digital Content 3, http:// links.lww.com/MS9/A172).
The percentage of patients that developed post-COVID complications was equal in all three waves. However, patients in the second wave developed more post-COVID cough and, memory disturbance, depression, and patients in the third wave developed more fatigue than those in the other waves (Supplement 3, Supplemental Digital Content 4, http://links.lww.com/MS9/    A173). The functional status of the patients was similar in the three waves. We found five symptom clusters. However, they did not differ among the three waves (Fig. 3).

Discussion
This study compared the first, second, and third COVID-19 waves in terms of presentation, outcomes, and hospital epidemiological trends. The first wave lasted longer; however, the second wave lasted for a short period. We found age and sex differences in the admission rates in the three waves. Cough, runny nose, respiratory distress, diarrhea, vomiting, and anosmia were the most common symptoms in the third wave. The symptom cluster differed among the three waves. Disease severity, duration of hospital stays, and severity markers differed. We found disparities in the frequencies of the positivity test and death rates. The three waves had similar post-COVID complications. This study was conducted at the tertiary center of Dhaka. The treatment facility differs from center to center and region to region; therefore, we cannot generalize the findings across the nation and globally.
During replication, a mutation in the genetic code of SARS-CoV-2 leads to the development of a new variant. The behaviors of variants, such as virulence and transmissibility, differ [17] . WHO named the variant to be monitored with the Greek alphabet and named from alpha to zeta (https://www.cdc.gov/ coronavirus/2019-ncov/variants/variant-classifications). In this country, until September 2021, alpha, beta, and delta variants were dominant at different times creating three waves of COVID-19; the first wave was caused by alpha, the second wave was caused by beta, and the third wave was caused by delta variants [18] . Epidemiological and disease characteristics differ in various studies [6,18,19] .
In our country, the first wave lasted~8 months, the second wave~2 months, and the third wave~3 months. This was due to the variations' transmissibility; delta was 63-167% more transmissible than alpha [20] . Therefore, the most susceptible people became infected faster, replacing existing variants. Delta and beta variants spread quickly in our country, and delta quickly became the dominant variant. Omicron was 2.8 times more transmissible than Delta, thus replacing Delta in a short period [21] .
In this study, we found a variation in the demographic patterns across the three variants.
Because of their increased mobility, the younger age group became infected in large numbers [22] . Immune senescence and various comorbidities could explain the older adults' admission. The viral receptors ACE2 and CD26 are more expressed in senescent cells, which explains why older adults are more susceptible to infection [23] . The virus affinity for the receptor varies among the variants. This explains the age group variations among variants [24] .
Men had a higher rate of incidence of COVID-19 due to the altered immunologic response, associated comorbidity, hormonal differences, and smoking habits [23] . Angiotensin-converting enzyme (ACE) receptor expression is higher in women because its genetic loci are on the X chromosome [25] . The TMPRSS2 gene is located on chromosome 21q22.3 [26] . Some variants have a higher affinity for TMPRSS than for ACE II. This might explain the sex differences in the expression of the various COVID-19 variants. Again, increased testosterone levels may increase the probability of microthrombi formation [27] , which is the underlying pathophysiology of severe COVID-19.
The duration of hospital admission was longer in the first wave. In the first wave, most patients were admitted because of isolation, and their release was determined by the time of PCR negativity. ICU admission was higher in the first wave due to the fear of an unnecessary patient transfer to the ICU. The death rate was higher in the third wave than in the other waves.
The different affinities of the viruses can explain the heterogeneous presentation and clustering of various variants of the receptor. The virus's ability to escape immunity [28] can explain the mutant strain's varied presentation and severity.
The three waves had slightly different post-COVID-19 complications. Further studies on its pathogenesis and immunological response are needed.
COVID-19 patients' presentation, outcomes, and epidemiologic trends have demonstrated remarkable variation across different waves, as confirmed by multiple studies conducted in various countries. Our study strongly supports this observation, and a solid pathophysiological basis exists for these differences.
There are some limitations to our study. First, it was conducted at a single center, and not all patients underwent genotyping due to practical limitations. Second, since it was a retrograde study and involved a review of documents, some information may need to be included.
In each of the three waves, COVID-19 occurred differently. A mutant strain of the SARS-CoV-2 was most likely the cause of these differences. Therefore, we recommend the following: • There should be genetic surveillance for the identification of the specific variant of interest.  • At the onset of each wave, intensive epidemiologic surveillance should be conducted. • There should be diversity in each wave's strategy for disease control planning, treatment, and hospital management.

Conclusion
We found differences in presentation, outcomes, and hospital epidemiologic trend of COVID-19 among the first, second, and third waves. So genetic surveillance is essential. In the future prospective multicenter research is warranted.

Ethical approval
The Institutional Ethical Committee of Dhaka Medical College approved the study (ERC-DMC/ECC/2021/55).

Patient consent
As we mainly reviewed hospital data, there was no scope for written consent. We took written or verbal consent from the patient for post-COVID follow-up.